Behavioral analysis for smart agents

ABSTRACT

A system and computer-readable storage medium perform a method for contextual inferring capacity for triggering a financial transaction by monitoring, via user device(s), objective contextual data of location, temporal, and volitional transaction information associated with an authorized user of a financial system. Subjective contextual data of personal calendar events, physiological data, and pacing of user interactions with the user device(s) is monitored. The objective and subjective contextual data is analyzed to create scenario(s) correlated with performing a volitional transaction. If not predictive a volitional transaction, a layer of security protocol is added for authentication prior to executing the volitional transaction. In response to determining that the current context is predictive of a volitional transaction, a determination is made whether the subjective contextual data satisfies criterion for incapacity to perform a volitional transaction. In response to determining incapacity to perform the volitional transaction, access to the financial system is limited.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/090,683, filed Nov. 5, 2020, and entitled “BEHAVIORAL ANALYSIS FORSMART AGENTS,” which is a continuation of U.S. patent application Ser.No. 15/724,765, filed Oct. 4, 2017, issued as U.S. Pat. No. 10,832,251,and entitled “BEHAVIORAL ANALYSIS FOR SMART AGENTS,” the entirety ofboth of which is incorporated herein by reference.

BACKGROUND

The present application relates to systems and methods forauthenticating and characterizing a human operator prior to facilitatingfinancial transactions.

Smart agents, as used by smartphones or other devices (e.g., AmazonAlexa, Google Now, iPhone's Siri), respond to specific requests forinformation, and may remind users about upcoming items on that user'scalendar. More sophisticated smart agents are starting to make attemptsto predict information that a user might need. For example, when anemail is received including flight information, information about theflight might be presented to the user on the day of the flight. Smartagents apparently do not, however, understand emotions or context whenperforming actions on behalf of users.

BRIEF DESCRIPTION

This brief description is provided to introduce a selection of conceptsin a simplified form that are described below in the detaileddescription. This brief description is not intended to be an extensiveoverview of the claimed subject matter, identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one or more embodiments, the subject disclosure provides a methodcomprising generating a predictive model trained with contextual dataassociated with volitional transactions performed by a user of afinancial system, and predicting, with the predictive model, alikelihood that a requested transaction of a user is volitional based oncurrent contextual data pertaining to the requested transaction. Themethod further comprises invoking an additional authentication protocolwhen the likelihood fails to meet a predetermined threshold, whereinsuccessful authentication by way of the additional authenticationprotocol is a condition precedent to permissible execution of therequested transaction, determining capacity of the user based on thecurrent contextual data when the likelihood meets the predeterminedthreshold, and controlling execution of the requested transaction basedon the capacity, wherein execution is at least limited when the user isdeemed incapacitated and otherwise permitted. Determining the capacitycan comprise determining whether subjective contextual data satisfies atleast one criterion for incapacity to perform the requested transaction.In one instance, the capacity can involve determining that thesubjective contextual data satisfies a rational state of mind criterion.In another instance, the capacity determination can include determiningthat the subjective contextual data satisfies an intoxication criterion.In yet another instance, capacity can be determined based on subjectivecontextual data satisfying an emotion agitation criterion. The methodcan further comprise blocking an advertisement that is correlated withcausing additional emotional agitation to a user as well as comprisingpresenting another advertisement that is correlated with soothing theemotional agitation. The method also comprises determining that thesubjective contextual data satisfies a third party criterion thatcorresponds to a third party who is at least one of verbally interactingwith the user or maintaining close proximity to the user indicative ofthe user being under duress. Further yet, in one instance, controllingexecution comprises placing a provisional hold on completion of thetransaction for a predetermined period of time.

In one or more embodiments, the subject disclosure provides a systemcomprising a processor coupled to a memory that stores instructionsthat, when executed by the processor, cause the processor to generate apredictive model trained with contextual data associated with volitionaltransactions performed by a user of a financial system, predict, withthe predictive model, a likelihood that a requested transaction of theuser is volitional based on current contextual data pertaining to therequested transaction, invoke an additional authentication protocol whenthe likelihood fails to meet a predetermined threshold, whereinsuccessful authentication by way of the additional authenticationprotocol is a condition precedent to permissible execution of therequested transaction, determine capacity of the user based on thecurrent contextual data when the likelihood meets the predeterminedthreshold, and control execution of the requested transaction based onthe capacity, wherein execution is at least limited when the user isdeemed incapacitated and otherwise permitted. Instructions can furthercause the processor to determine the capacity based on whethersubjective contextual data satisfies at least one criterion forincapacity to perform the requested transaction. Additionally, theinstructions can cause the processor to determine whether the subjectivecontextual data satisfies the at least one criterion for incapacitybased on whether a current pacing of interactions of the user on acomputing device is below a threshold with respect to a baseline pacingof interaction. In other instance, the instructions can cause theprocessor to determine that the subjective contextual data satisfies anemotional agitation criterion, as well as identify an advertisement thatcorrelates with causing further emotional agitation and replace theadvertisement with another advertisement that is correlated withsoothing the emotional agitation. Furthermore, the instructions cancause the processor to control execution by placing a provisional holdon completion of the requested transaction for a predetermined period oftime. In one instance, the provided predictive model can learncontextual scenarios that are associated with a volitional transactionto enable predictions.

In one or more embodiments, the subject disclosure provides a methodthat comprises executing, on a processor, instructions that cause theprocessor to perform a plurality of operations. The operations includeinvoking a predictive model to predict a likelihood that a requestedtransaction by a user of a financial system is volitional based oncurrent contextual data collected by one or more computing devices ofthe user, wherein the predictive model is trained with contextual dataassociated with one or more transactions performed by the user, invokingan additional authentication protocol when the likelihood fails to meeta predetermined threshold, and controlling processing of the requestedtransaction based on the likelihood and authentication success orfailure with the additional authentication protocol. Operations canfurther include determining capacity of a user when the likelihood meetsthe predetermined threshold based on subjective contextual data andrestricting the processing of the requested transaction when the user isdetermined to be incapacitated. In one instance, a user can be deemed tobe incapacitated based on satisfaction of at least one predeterminedcriterion for incapacity associated with rational decision making.Further, a user can be determined to be incapacitated based onsatisfaction of at least one predetermined criterion comprisingintoxication or emotional agitation.

The following description and annexed drawings set forth certainillustrative aspects and implementations. These are indicative of but afew of the various ways in which one or more aspects may be employed.Other aspects, advantages, or novel features of the disclosure willbecome apparent from the following detailed description when consideredin conjunction with the annexed drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure are understood from the following detaileddescription when read with the accompanying drawings. Elements,structures, etc. of the drawings may not necessarily be drawn to scale.Accordingly, the dimensions of the same may be arbitrarily increased orreduced for clarity of discussion, for example.

FIG. 1 illustrates a block diagram of a system for contextualauthentication and transaction control, according to one or moreembodiments;

FIG. 2 illustrates a block diagram of a communication system forcontextual authentication and control of financial transactions,according to one or more embodiments;

FIG. 3 illustrates a block diagram of a communication system fordetecting a fully capable, authenticated user, an imposter user, anincapacitated user, and an emotionally agitated user, according to oneor more embodiments;

FIG. 4 illustrates a block diagram of a system having a computing devicethat performs contextual user authentication and control according toone or more embodiments;

FIG. 5 illustrates a block diagram of example computer-readable mediumor computer-readable device including processor-executable instructionsconfigured to embody one or more of the provisions set forth herein,according to one or more embodiments; and

FIG. 6 illustrates a flow diagram of a method of contextually predictingbehavior of a human operator and controlling financial transactions,according to one or more embodiments.

DETAILED DESCRIPTION

A system and computer-readable storage medium perform a method forcontextual inferring capacity for triggering a financial transaction bymonitoring, via user device(s), objective contextual data of location,temporal, and volitional transaction information associated with anauthorized user of a financial system. Subjective contextual data ofpersonal calendar events, physiological data, and pacing of userinteractions with the user device(s) is monitored. The objective andsubjective contextual data is analyzed to create scenario(s) correlatedwith performing a volitional transaction. If not predictive of avolitional transaction, a layer of security protocol is added forauthentication prior to executing the volitional transaction. Inresponse to determining that the current context is predictive of avolitional transaction, a determination is made whether the subjectivecontextual data satisfies criterion for incapacity to perform avolitional transaction. In response to determining incapacity to performthe volitional transaction, access to the financial system is limited.

Embodiments or examples, illustrated in the drawings are disclosed belowusing specific language. It will nevertheless be understood that theembodiments or examples are not intended to be limiting. Any alterationsand modifications in the disclosed embodiments, and any furtherapplications of the principles disclosed in this document arecontemplated as would normally occur to one of ordinary skill in thepertinent art.

The following terms are used throughout the disclosure, the definitionsof which are provided herein to assist in understanding one or moreaspects of the disclosure.

As used herein, the term “infer” or “inference” generally refer to theprocess of reasoning about or inferring states of a system, a component,an environment, a user from one or more observations captured via eventsor data, etc. Inference may be employed to identify a context or anaction or may be employed to generate a probability distribution overstates, for example. An inference may be probabilistic. For example,computation of a probability distribution over states of interest basedon a consideration of data or events. Inference may also refer totechniques employed for composing higher-level events from a set ofevents or data. Such inference may result in the construction of newevents or new actions from a set of observed events or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

Turning to the Drawings, FIG. 1 illustrates a system 100 has anobjective contextual monitoring component 102 that is in communicationwith a one or more user devices 104 associated with an authorized user105. The objective contextual monitoring component 102 receivesobjective contextual data comprising more than one of: (i) locationinformation; (ii) temporal information; and (iii) volitional transactioninformation associated with an authorized user of a financial system. Asubjective contextual monitoring component 106 is in communication withthe one or more user devices 104 to receive subjective contextual datacomprising more than one of: (i) personal calendar events; (ii)physiological data; and (iii) pacing of user interactions with the oneor more user devices. A contextual analyzing component 108 receives theobjective and subjective contextual data and creates scenario(s) thatare correlated with performing a volitional transaction. A predictiveengine 110 determines whether a current context is one of thescenario(s) that is correlated with and predictive of the volitionaltransactional information. A financial transaction controller 112 actson predictive analyses. In response to determining that the currentcontext is not predictive of a volitional transaction, the financialtransaction controller 112 adds a layer of security protocol for userauthentication prior to executing the volitional transaction. Inresponse to determining that the current context is predictive of avolitional transaction, the financial transaction controller 112determines whether the subjective contextual data satisfies at least onecriterion for incapacity to perform a volitional transaction. Inresponse to determining an incapacity to perform the volitionaltransaction, the financial transaction controller 112 limits access tothe financial system.

In one or more embodiments, FIG. 2 illustrates a system 200 executed atleast in part by one or more user devices, illustrated as a smartphone202, blue tooth earpiece 204, and smart watch 206 that form a personalaccess network (PAN) 208. A number of Internet of Things (IoT) devicescan provide information to an IoT contextual tracking module 210. Forexample, the smartphone 202 can contain or be in communication one ormore of the following sources of contextual information: (i)physiological data sensors 212; (ii) audio sensor 214; (iii) infraredsensor 216; (iv) location sensor 218; (v) personal calendar 220; (vi)camera monitor 222; (vii) transaction monitor 224; (viii) communicationtracking 226; and (ix) biometric sensor 228.

Based on the information collected by the IoT contextual tracking module210, a number of learned contexts can be amassed by a learned contextmodule 230, such as a “Town A work context”, “Town B home context”, and“Town B shopping context”, “Town C vacation context” 230 can havecertain contexts that are too incomplete for doing predictive analyses,represented by “Town D Unfamiliar” context.

Subjective aspects of a historical or current context can be determinedby an emotional and cognitive state analysis module 232. Examples ofconstituent capabilities of module 232 are illustrated as a facialrecognition component 234, a voice recognition/stress analyzer component236, a remote temperature sensor 238, a context correlator 240, and auser interface pacing component 242.

An executive contextual control module 244 can act upon the objectiveand subjective contextual information to trigger an authenticationcomponent 246, a transaction agent 248, and a report component 250 asneeded. For example, the executive contextual control module 244 candetermine that a current contextual scenario falls within amultidimensional space defined within an access control module 252. Forexample, the contextual response can be a function of an inferred userauthentication value, an inferred emotional state value and an inferredcognitive capacity value. For example, when fully authenticated withhigh values of cognitive and emotional state, full transactional accesscan be granted. When not authenticated, additional layers of securitycan be imposed. When in a reduced capacity, transactions can be limited,such as authorizing transportation for an inebriated user but notauthorizing large banking transactions. When in an agitated state, thesystem can be discrete in what types of financial transactions oradvertisements are offered to not exacerbate the situation.

In one or more embodiments, FIG. 3 illustrates a communication system300 having a network 302 that couples sources of data and processingcapabilities to perform contextual collection, analysis and predictionof a user 304. One or more financial transaction sources 306 providepast or current financial transactions, either open for completion by ahuman operator 304 or previously completed. A system manager 308 canobtain relevant financial transactions 310 from the financialtransactions sources 306 and provide filtered financial transactions 312to the transaction analyzing component 314 of a simulacrum engine 316.The simulacrum engine 316 can create and maintain a predictive model 318for the human operator 304 that is combined with contextual information.The predictive model 318 is contained in a simulacrum engine database320. Mobile application 322 executed on a user device 324 can provide auser interface 326 for providing recommendations 328 for and forreceiving activation of control affordances 330 from the human operator304 and for collecting objective and subjective contextual data. Theuser device 324 itself can contain sensors and communication channelsthat enable monitoring of a capacity status of the human operator 304.For clarity, external sources of information regarding the health,location, activity level, cognitive interactions, etc., by the humanoperator 304 are illustrated as a camera 332, a smart watch 334, and ablue tooth device 336, or other Internet of Things (IoT) devices. Thus,an ability is provided to capture health and mobility levels of thehuman operator. This ability could leverage other devices used tomonitor an individual's health in general (e.g., activity trackers,heart rate monitors). The mobile application 322 can communicate via acommunication channel 338 to a node 340 that in turn is communicativelycoupled to the network 302. Based on this information, the system 300can differentiate situations such as full capable user 304, an imposter342, an incapacitated user 344, and an enraged, irrational user 346.

FIG. 4 and the following discussion provide a description of a suitablecomputing environment to implement embodiments of one or more of theprovisions set forth herein. The operating environment of FIG. 4 ismerely one example of a suitable operating environment and is notintended to suggest any limitation as to the scope of use orfunctionality of the operating environment. Example computing devicesinclude, but are not limited to, personal computers, server computers,hand-held or laptop devices, mobile devices, such as mobile phones,Personal Digital Assistants (PDAs), media players, and the like,multiprocessor systems, consumer electronics, mini computers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, etc.

Generally, embodiments are described in the general context of “computerreadable instructions” being executed by one or more computing devices.Computer readable instructions may be distributed via computer readablemedia as will be discussed below. Computer readable instructions may beimplemented as program modules, such as functions, objects, ApplicationProgramming Interfaces (APIs), data structures, and the like, thatperform one or more tasks or implement one or more abstract data types.Typically, the functionality of the computer readable instructions arecombined or distributed as desired in various environments.

FIG. 4 illustrates a system 400 including a computing device 412configured to implement one or more embodiments provided herein. In oneconfiguration, computing device 412 includes at least one processingunit 416 and memory 418. Depending on the exact configuration and typeof computing device, memory 418 may be volatile, such as RAM,non-volatile, such as ROM, flash memory, etc., or a combination of thetwo. This configuration is illustrated in FIG. 4 by dashed line 414.

In other embodiments, device 412 includes additional features orfunctionality. For example, device 412 may include additional storagesuch as removable storage or non-removable storage, including, but notlimited to, magnetic storage, optical storage, etc. Such additionalstorage is illustrated in FIG. 4 by storage 3420. In one or moreembodiments, computer readable instructions to implement one or moreembodiments provided herein are in storage 420. Storage 420 may storeother computer readable instructions to implement an operating system,an application program, etc. Computer readable instructions may beloaded in memory 418 for execution by processing unit 416, for example.

The term “computer readable media” as used herein includes computerstorage media. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions or other data. Memory 418 and storage 420 are examples ofcomputer storage media. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, Digital Versatile Disks (DVDs) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by device 412. Anysuch computer storage media is part of device 412.

The term “computer readable media” includes communication media.Communication media typically embodies computer readable instructions orother data in a “modulated data signal” such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” includes a signal that has one or more ofits characteristics set or changed in such a manner as to encodeinformation in the signal.

Device 412 includes input device(s) 424 such as keyboard, mouse, pen,voice input device, touch input device, infrared cameras, video inputdevices, or any other input device. Output device(s) 422 such as one ormore displays, speakers, printers, or any other output device may beincluded with device 412. Input device(s) 424 and output device(s) 422may be connected to device 412 via a wired connection, wirelessconnection, or any combination thereof. In one or more embodiments, aninput device or an output device from another computing device may beused as input device(s) 424 or output device(s) 422 for computing device412. Device 412 may include communication connection(s) 426 tofacilitate communications via a network 428 with one or more othercomputing devices 430.

Certain functionalities can be performed by software applicationsresident in memory 418, such as a transactional analyzing component 432,simulacrum engine 434, communications monitoring component 436, and asimulacrum avatar 438.

Still another embodiment involves a computer-readable medium includingprocessor-executable instructions configured to implement one or moreembodiments of the techniques presented herein. An embodiment of acomputer-readable medium or a computer-readable device devised in theseways is illustrated in FIG. 5 , wherein an implementation 500 includes acomputer-readable medium 508, such as a CD-R, DVD-R, flash drive, aplatter of a hard disk drive, etc., on which is encodedcomputer-readable data 506. This computer-readable data 506, such asbinary data including a plurality of zero's and one's as shown in 506,in turn includes a set of computer instructions 504 configured tooperate according to one or more of the principles set forth herein. Inone such embodiment 500, the processor-executable computer instructions504 may be configured to perform a method 502, such as method 600 ofFIG. 6 . In another embodiment, the processor-executable instructions504 may be configured to implement a system, such as the system 400 ofFIG. 4 . Many such computer-readable media may be devised by those ofordinary skill in the art that are configured to operate in accordancewith the techniques presented herein.

FIG. 6 illustrates a method 600 of contextual inferring capacity fortriggering a financial transaction. In one or more embodiments, themethod 600 begins monitoring, via one or more user devices, objectivecontextual data comprising more than one of: (i) location information;(ii) temporal information; and (iii) volitional transaction informationassociated with an authorized user of a financial system (block 602).Method 600 includes monitoring, via one or more user devices, subjectivecontextual data comprising more than one of: (i) personal calendarevents; (ii) physiological data; and (iii) pacing of user interactionswith the one or more user devices (block 604). Method 600 includesanalyzing the objective and subjective contextual data to create one ormore scenarios that are correlated with performing a volitionaltransaction (block 606). Method 600 includes determining whether acurrent context is one of the one or more scenarios that is correlatedwith and predictive of the volitional transactional information(decision block 608). In response to determining that the currentcontext is not predictive of a volitional transaction, method 600includes adding a layer of security protocol for user authenticationprior to executing the volitional transaction (block 610). Then method600 ends. In response to determining that the current context ispredictive of a volitional transaction in decision block 608, method 600includes determining whether the subjective contextual data satisfies atleast one criterion for incapacity to perform a volitional transaction(decision block 612). In response to determining an incapacity toperform the volitional transaction, method 600 includes limiting accessto the financial system (block 614). Then method 600 ends. In responseto not determining an incapacity to perform the volitional transactionin decision block 612, method 600 ends.

In one or more embodiments, the criterion for incapacity isintoxication; and method 600 includes limiting access to the financialsystem by: (i) presenting a cognitive sobriety test via at least oneuser device; and (ii) preventing access to the financial system inresponse to failing the cognitive sobriety test.

In one or more embodiments, the criterion for incapacity is emotionalagitation. Method 600 includes limiting access to the financial systemby blocking at least one category of financial transaction associatedwith a policy having a condition precedent of being in a rational stateof mind. In a particular embodiment, the criterion further includesdetecting a third party who is at least one of: (i) verbally interactingwith the authorized user; and (ii) maintaining close proximity to theauthorized user indicative of the authorized user being under duress.Method 600 includes limiting access to the financial system by: (i)presenting on a user interface execution of the financial transaction;and (ii) placing a provisional hold on actual completion of thefinancial transaction for a period of time.

In one or more embodiments, the criterion for incapacity comprisesemotional agitation. Method 600 includes limiting access to thefinancial system by: (i) blocking an advertisement, via the one or moreuser devices, that has a correlation with causing additional agitationto the authorized user; and (ii) presenting another advisement, via theone or more user devices, that has a correlation with soothing theemotional agitation of the authorized user.

In one or more embodiments, method 600 includes: (i) monitoring thepacing of user interactions with the one or more user devices comprisesdetermining a baseline pacing of user interactions; and (ii) determiningwhether the subjective contextual data satisfies the at least onecriterion for incapacity comprises determining whether a current pacingis below a threshold fraction of the baseline pacing. In a particularembodiment, the criterion for incapacity is emotional agitation. Method600 includes limiting access to the financial system by: (i) blocking anadvertisement, via the one or more user devices, that has a correlationwith causing additional agitation to the authorized user; and (ii)presenting another advisement, via the one or more user devices, thathas a correlation with soothing the emotional agitation of theauthorized user.

According to aspects of the present innovation, behavioral analysis isprovided for smart devices that leverages the fact that everyone has aunique communication footprint. The information gleaned therefrom can beused for fraud prevention, authentication, creation of user profiles,and adjusting smart device behavior for different individuals. Forexample, a smart device might attempt to get an “emotional read” on aperson. Based on knowing when the person is upset or tired, the systemadjusts smart device behavior or marketing messages accordingly.

In one or more aspects, a smart agent (or virtual assistant) acts in amore sophisticated manner by incorporating context into actionsperformed on behalf of a user. Using context for fraud prevention,authentication improves services performed by the smart agent on behalfof a user and can provide more effective marketing messages.

According to aspects of the present innovation, a smart agent acts in amore sophisticated manner by incorporating context into actionsperformed on behalf of a user, as would an actual human being. Forexample, a smart agent might perform an analysis over the course ofweeks or months to determine certain traits associated with the user.

In an illustrative example, a smart agent can determine the followingtraits: (i) The user habitually calls a relative each Sunday night; (ii)The user tends to pay bills each Thursday evening; (iii) The user tendsto access television listing information at the “tvlistings.com” websitewhen the television is first turned on; (iv) the user tends to becomeangry when talking to a particular person or when a particular topic isdiscussed.

Based on such contextual learning, the system can performauthentication. If the user does not perform a task he or she normallyperforms on Sunday evenings (e.g., calling a relative), the smart agentmight require additional authentication credentials to ensure that theuser is the authorized user

As another example, the system can provide fraud prevention. If the userattempts to pay bills at an unusual time (e.g., other than on Thursdayevening), further authentication could be required before grantingaccess to financial information

As an additional example, the system can provide services related tocontext. When the smart agent determines, based its audio sensors, thatthe television was just turned on, the smart agent might automaticallyprovide television listing information from the tvlistings.com website.

As a further example, the system can provide enhanced marketing. Whenthe smart agent determines that the user is angry or emotional, thesmart agent might refrain from presenting marketing messages. By benefitof the present disclosure, contextual learning is used to determinebehaviors or traits for authentication, and fraud prevention. Use ofdetermined emotions can better leverage marketing content forappropriate situations.

One or more embodiments may employ various artificial intelligence (AI)based schemes for carrying out various aspects thereof. One or moreaspects may be facilitated via an automatic classifier system orprocess. A classifier is a function that maps an input attribute vector,x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to aclass. In other words, f(x)=confidence (class). Such classification mayemploy a probabilistic or statistical-based analysis (e.g., factoringinto the analysis utilities and costs) to prognose or infer an actionthat a user desires to be automatically performed.

A support vector machine (SVM) is an example of a classifier that may beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which the hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that may be similar, but notnecessarily identical to training data. Other directed and undirectedmodel classification approaches (e.g., naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models) providing different patterns of independence maybe employed. Classification as used herein, may be inclusive ofstatistical regression utilized to develop models of priority.

One or more embodiments may employ classifiers that are explicitlytrained (e.g., via a generic training data) as well as classifiers whichare implicitly trained (e.g., via observing user behavior, receivingextrinsic information). For example, SVMs may be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, a classifier may be used to automatically learnand perform a number of functions, including but not limited todetermining according to a predetermined criteria.

As used in this application, the terms “component”, “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,or a computer. By way of illustration, both an application running on acontroller and the controller may be a component. One or more componentsresiding within a process or thread of execution and a component may belocalized on one computer or distributed between two or more computers.

Further, the claimed subject matter is implemented as a method,apparatus, or article of manufacture using standard programming orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter of the appended claims is not necessarily limited tothe specific features or acts described above. Rather, the specificfeatures and acts described above are disclosed as example embodiments.

Various operations of embodiments are provided herein. The order inwhich one or more or all of the operations are described should not beconstrued as to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated based on thisdescription. Further, not all operations may necessarily be present ineach embodiment provided herein.

As used in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or”. Further, an inclusive “or” may includeany combination thereof (e.g., A, B, or any combination thereof). Inaddition, “a” and “an” as used in this application are generallyconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form. Additionally, at least one ofA and B and/or the like generally means A or B or both A and B. Further,to the extent that “includes”, “having”, “has”, “with”, or variantsthereof are used in either the detailed description or the claims, suchterms are intended to be inclusive in a manner similar to the term“comprising”.

Further, unless specified otherwise, “first”, “second”, or the like arenot intended to imply a temporal aspect, a spatial aspect, an ordering,etc. Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first channel and asecond channel generally correspond to channel A and channel B or twodifferent or two identical channels or the same channel. Additionally,“comprising”, “comprises”, “including”, “includes”, or the likegenerally means comprising or including, but not limited to.

Although the disclosure has been shown and described with respect to oneor more implementations, equivalent alterations and modifications willoccur based on a reading and understanding of this specification and theannexed drawings. The disclosure includes all such modifications andalterations and is limited only by the scope of the following claims.

What is claimed is:
 1. A method comprising: receiving, by one or morecomputing devices contextual data associated with volitionaltransactions performed by a user of a financial system, collected by oneor more sensor devices; generating, by the one or more computingdevices, a predictive model trained with the contextual data;generating, by the one or more computing devices, one or more scenarioscorrelated with and predictive of a volitional transaction of the uservia the predictive model; predicting, by the one or more computingdevices and with the predictive model, a likelihood that a requestedtransaction of a user is volitional based on data collected by the oneor more sensor devices; invoking, by the one or more computing devices,an additional authentication protocol when the likelihood fails to meeta predetermined threshold, wherein successful authentication by way ofthe additional authentication protocol is a condition precedent topermissible execution of the requested transaction; determining, by theone or more computing devices, capacity of the user based on currentcontextual data when the likelihood meets the predetermined threshold;and controlling, by the one or more computing devices, execution of therequested transaction based on the capacity, wherein execution is atleast limited when the user is deemed incapacitated and otherwisepermitted, wherein controlling execution of the requested transactioncomprises: presenting, by the one or more computing devices on a userinterface, an execution of a financial transaction; and placing for aperiod of time, by the one or more computing devices, a provisional holdon completion of the financial transaction.
 2. The method of claim 1,wherein determining the capacity comprises determining whethersubjective contextual data satisfies at least one criterion forincapacity to perform the requested transaction.
 3. The method of claim2, further comprising determining, by the one or more computing devices,that the subjective contextual data satisfies a rational state of mindcriterion.
 4. The method of claim 2, further comprising determining, bythe one or more computing devices, that the subjective contextual datasatisfies an intoxication criterion.
 5. The method of claim 2, furthercomprising determining, by the one or more computing devices, that thesubjective contextual data satisfies an emotion agitation criterion. 6.The method of claim 5, further comprising blocking, by the one or morecomputing devices, an advertisement that is correlated with causingadditional emotional agitation to the user.
 7. The method of claim 6,further comprising presenting, by the one or more computing devices,another advertisement that is correlated with soothing the emotionalagitation.
 8. The method of claim 2, further comprising determining, bythe one or more computing devices, that the subjective contextual datasatisfies a third-party criterion corresponding to a third party who isat least one of: verbally interacting with the user or maintaining closeproximity to the user.
 9. A system comprising: one or more processorsconfigured to: receive contextual data associated with volitionaltransactions performed by a user of a financial system, collected by oneor more sensor devices; generate a predictive model trained with thecontextual data; generate one or more scenarios correlated with andpredictive of a volitional transaction of the user via the predictivemodel; predict a likelihood that a requested transaction of a user isvolitional based on data collected by the one or more sensor devices;invoke an additional authentication protocol when the likelihood failsto meet a predetermined threshold, wherein successful authentication byway of the additional authentication protocol is a condition precedentto permissible execution of the requested transaction; determinecapacity of the user based on current contextual data when thelikelihood meets the predetermined threshold; and control execution ofthe requested transaction based on the capacity, wherein execution is atleast limited when the user is deemed incapacitated and otherwisepermitted, wherein controlling execution of the requested transactioncomprises: present an execution of a financial transaction; and placefor a period of time a provisional hold on completion of the financialtransaction.
 10. The system of claim 9, wherein determining the capacitycomprises determining whether subjective contextual data satisfies atleast one criterion for incapacity to perform the requested transaction.11. The system of claim 10, wherein the one or more processors arefurther configured to determine that the subjective contextual datasatisfies a rational state of mind criterion.
 12. The system of claim10, wherein the one or more processors are further configured todetermine that the subjective contextual data satisfies an intoxicationcriterion.
 13. The system of claim 10, wherein the one or moreprocessors are further configured to determine that the subjectivecontextual data satisfies an emotion agitation criterion.
 14. The systemof claim 13, wherein the one or more processors are further configuredto block an advertisement that is correlated with causing additionalemotional agitation to the user.
 15. The system of claim 14, wherein theone or more processors are further configured to present anotheradvertisement that is correlated with soothing the emotional agitation.16. The system of claim 15, wherein the one or more processors arefurther configured to determine that the subjective contextual datasatisfies a third-party criterion corresponding to a third party who isat least one of: verbally interacting with the user or maintaining closeproximity to the user.
 17. A non-transitory computer readable mediumcomprising program code that when executed by one or more processorscauses the one or more processors to: receive contextual data associatedwith volitional transactions performed by a user of a financial system,collected by one or more sensor devices; generate a predictive modeltrained with the contextual data; generate one or more scenarioscorrelated with and predictive of a volitional transaction of the uservia the predictive model; predict a likelihood that a requestedtransaction of a user is volitional based on data collected by the oneor more sensor devices; invoke an additional authentication protocolwhen the likelihood fails to meet a predetermined threshold, whereinsuccessful authentication by way of the additional authenticationprotocol is a condition precedent to permissible execution of therequested transaction; determine capacity of the user based on currentcontextual data when the likelihood meets the predetermined threshold;and control execution of the requested transaction based on thecapacity, wherein execution is at least limited when the user is deemedincapacitated and otherwise permitted, wherein controlling execution ofthe requested transaction comprises: present an execution of a financialtransaction; and place for a period of time a provisional hold oncompletion of the financial transaction.
 18. The non-transitory computerreadable medium of claim 17, wherein determining the capacity comprisesdetermining whether subjective contextual data satisfies at least onecriterion for incapacity to perform the requested transaction.
 19. Thenon-transitory computer readable medium of claim 18, wherein the atleast one criterion for incapacity comprises one or more of: a rationalstate of mind criterion, an intoxication criterion, or an emotionagitation criterion.
 20. The non-transitory computer readable medium ofclaim 18, further comprising program code that when executed by the oneor more processors causes the one or more processors to determine thatthe subjective contextual data satisfies a third-party criterioncorresponding to a third party who is at least one of: verballyinteracting with the user or maintaining close proximity to the user.